change in exp

cornac/experiment/experiment.py

@@ -91,9 +91,9 @@ def run(self):
         organized_metrics = {'ranking': [], 'rating': []}
 
         # Organize metrics into "rating" and "ranking" for efficiency purposes
-        #for mt in self.metrics:
-        #    organized_metrics[mt.type].append(mt)
-        #    metric_names.append(mt.name)
+        for mt in self.metrics:
+            organized_metrics[mt.type].append(mt)
+            metric_names.append(mt.name)
 
         for model in self.models:
             if self.verbose:

examples/c2pf_example.py

@@ -10,20 +10,20 @@
 from cornac.experiment import Experiment
 from cornac import metrics
 from cornac.models import C2PF
-import numpy as np
+from cornac.datasets import amazon_office as office
 
 # Load office ratings and item contexts, see C2PF paper for details
 ratings = office.load_rating()
 contexts = office.load_context()
 
-item_graph_module = GraphModule(data=office_context)
+item_graph_module = GraphModule(data=contexts)
 
-ratio_split = RatioSplit(data=office_ratings,
+ratio_split = RatioSplit(data=ratings,
                          test_size=0.2, rating_threshold=3.5,
                          shuffle=True, exclude_unknowns=True,
                          verbose=True, item_graph=item_graph_module)
 
-rec_c2pf = C2PF(k=100, max_iter=80, variant='c2pf')
+c2pf = C2PF(k=100, max_iter=1, variant='c2pf')
 
 # Evaluation metrics
 nDgc = metrics.NDCG(k=-1)
@@ -33,6 +33,6 @@
 
 # Instantiate and run your experiment
 exp = Experiment(eval_method=ratio_split,
-                 models=[rec_c2pf],
+                 models=[c2pf],
                  metrics=[nDgc, mrr, rec, pre])
 exp.run()